DE.CM-09: Computing hardware and software, runtime environments, and their data are monitored to find potentially adverse events

To meet de.cm-09: computing hardware and software, runtime environments, and their data are monitored to find potentially adverse events requirement, you must implement continuous, risk-based monitoring across endpoints, servers, cloud workloads/containers, and key data stores, then route actionable detections to triage and response with retained evidence. The fastest path is standard telemetry, centralized logging, alert rules tied to “adverse events,” and a documented review cadence. ¹

Key takeaways:

• Cover the full stack: hardware/endpoints, software, runtime environments (VMs, containers, serverless), and the data they handle. ¹
• Monitoring is a control you must operate: define what you watch, how alerts are handled, and what evidence proves it. ²
• Auditors will test completeness (coverage) and effectiveness (triage + follow-up), not just tool deployment. ³

DE.CM-09 is a detection engineering and operations requirement disguised as a single sentence. It expects you to monitor computing hardware and software, runtime environments, and their data for “potentially adverse events,” then prove that monitoring is real, repeatable, and acted on. ¹

For a Compliance Officer, CCO, or GRC lead, the operational goal is simple: convert “we have tools” into an auditable monitoring control with defined scope, telemetry standards, alerting logic, triage workflows, and review evidence. This is where programs often fail. Teams deploy EDR, cloud logging, and a SIEM, but cannot show coverage, cannot explain what counts as “adverse,” and cannot demonstrate that alerts drive outcomes (tickets, containment, lessons learned). ²

This page gives you requirement-level implementation guidance you can hand to security operations, cloud/platform engineering, and IT. You’ll get a step-by-step build plan, the evidence bundle to retain, common audit traps, and a practical execution plan to stand this up quickly without boiling the ocean. ¹

Regulatory text

Excerpt (DE.CM-09): “Computing hardware and software, runtime environments, and their data are monitored to find potentially adverse events.” ¹

What the operator must do:
You must (1) define which assets and environments are in scope (hardware, software, runtime environments, and related data), (2) collect security-relevant telemetry from them, (3) analyze telemetry to detect potentially adverse events, and (4) operationalize response so detections are reviewed, escalated, and tracked with evidence. ²

Plain-English interpretation (what “good” looks like)

• “Computing hardware and software” means endpoints, servers, network-attached systems that run workloads, and the operating systems and applications on them.
• “Runtime environments” means the layers where code executes: VMs, containers/Kubernetes, serverless, managed PaaS services, and key control planes.
• “And their data” means logs and telemetry, but also the data stores those systems touch where suspicious access or changes can signal harm.
• “Monitored to find potentially adverse events” means you can detect meaningful security conditions (misuse, compromise, policy violations, abnormal changes), not just collect logs. ¹

A practical compliance translation: you should be able to show an auditor a coverage map, a set of detections aligned to risks, and proof of recurring review and remediation when monitoring fails or alerts indicate issues. ²

Who it applies to (entity and operational context)

This applies to any organization claiming alignment to NIST CSF 2.0 outcomes, including:

• Critical infrastructure operators with operational technology, enterprise IT, and hybrid environments. ²
• Service organizations (including SaaS and managed services) where customer trust depends on detection and response. ²
• Organizations with formal cybersecurity programs that must demonstrate detection coverage and operational discipline. ²

Operationally, DE.CM-09 is triggered any time you have:

• A mix of endpoints/servers and cloud workloads,
• Production applications with privileged access paths,
• Containers, serverless, or managed runtime services,
• Third-party-provided platforms where you still have monitoring responsibilities (shared responsibility model). ¹

What you actually need to do (step-by-step)

Step 1: Define “potentially adverse events” for your environment

Create a short, controlled list of event categories tied to your risks and response capability. Examples:

• Privilege escalation or unusual admin actions
• Suspicious authentication patterns (impossible travel, repeated failures, token misuse)
• Endpoint malware/behavioral detections
• Lateral movement indicators
• Cloud control plane changes (IAM policy edits, logging disabled)
• Container/Kubernetes: exec into pod, privileged containers, image drift
• Data store anomalies: bulk reads, unexpected exports, encryption key misuse

Output: “DE.CM-09 Adverse Event Catalog” with owners and severity definitions. ²

Step 2: Set monitoring scope and minimum telemetry standards

Build a scope statement that is audit-friendly:

• In-scope asset classes (endpoints, servers, cloud accounts/subscriptions, clusters, critical SaaS)
• In-scope data stores (customer data platforms, regulated data repositories, secrets stores)
• Minimum telemetry per class (auth logs, process execution, network connections, admin activity)

Output: “Monitoring Coverage Matrix” mapping asset classes to telemetry sources and collection method. ¹

Step 3: Centralize logs and define retention aligned to investigations

You need centralized collection (SIEM/log analytics) or a defensible equivalent with:

• Consistent timestamps, asset identity, user identity
• Integrity controls for logs (to reduce tampering risk)
• Retention long enough to support detection and investigations (set a policy; don’t guess in an audit)

Output: Logging architecture diagram + logging standard + retention configuration evidence. ²

Step 4: Implement detection content and routing

For each adverse event category, define:

• Detection logic (rule, analytics, correlation, EDR alert)
• Data dependencies (which logs required)
• Expected response (ticket, page, containment action)
• False positive handling (tuning criteria)

Route detections into a system of record (ticketing/case management). If you use a SOAR workflow, ensure it produces auditable artifacts (case notes, timestamps, actions). ²

Output: Detection register: rule name, purpose, data source, owner, last review date, and change control reference.

Step 5: Operationalize triage, escalation, and exception handling

Write a one-page operating procedure:

• Who monitors alerts (SOC, IT, on-call)
• Triage steps and decision points
• Escalation triggers (what gets raised to incident response)
• SLAs you can actually meet (avoid aspirational targets you can’t evidence)
• Exceptions: what happens when a log source is down, an agent is missing, or a cloud account is unmanaged

Output: “Monitoring & Triage SOP” + exception workflow with remediation ownership. ²

Step 6: Prove the control runs: reviews, metrics, and management oversight

Run recurring control performance reviews that answer:

• Coverage: which critical assets are not reporting?
• Effectiveness: are detections firing, and are they meaningful?
• Hygiene: are there noisy rules nobody triages?
• Resilience: did monitoring gaps occur, and how fast were they fixed?

Capture decisions and follow-ups in a lightweight evidence bundle. This is where tools like Daydream fit naturally: keep the DE.CM-09 requirement mapped to owners, metrics, review notes, and exceptions so you can answer due diligence and audits without scrambling. ²

Required evidence and artifacts to retain (auditor-ready)

Maintain an evidence bundle that a reviewer can validate without reverse-engineering your SOC:

1. Monitoring Coverage Matrix (asset classes → telemetry sources → collection status/owner)
2. Adverse Event Catalog (definitions, severity, escalation path)
3. Logging standard (required fields, time sync approach, tagging, retention policy)
4. System configurations/screenshots/exports showing key log sources enabled (cloud audit logs, EDR rollout status, container audit logging)
5. Detection register (rules/alerts, data source, owner, last review, tuning notes)
6. Sample alert-to-case evidence (alerts that produced tickets/cases with disposition)
7. Control performance review records (agenda, metrics, exceptions, remediation plans and due dates)
8. Exception log (gaps, compensating controls, closure evidence)

These artifacts directly address the common gap NIST programs face: conceptual alignment without measurable, provable operation. ²

Common exam/audit questions and hangups

Expect questions like:

• “Show me your monitoring scope. How do you know all production workloads are covered?”
• “Which runtime environments do you run, and what telemetry do you collect from each?”
• “How do you detect unauthorized changes to logging configuration?”
• “Prove alerts are reviewed. Show a sample from detection to closure.”
• “What happens when logging breaks? Who gets notified, and how is it tracked?”
• “How do you monitor third-party-hosted systems under shared responsibility?”

Hangups auditors see:

• No single owner can explain coverage end-to-end (endpoint team vs cloud team vs app team).
• Monitoring exists, but triage is informal (no case notes, no dispositions).
• Retention is unknown or changes without approval. ¹

Frequent implementation mistakes (and how to avoid them)

Mistake	Why it fails DE.CM-09	What to do instead
“We have a SIEM” as the whole control	A SIEM without defined scope and detections is log storage	Maintain a coverage matrix + detection register tied to adverse event categories 2
Ignoring runtime specifics (containers/serverless)	You miss control plane and ephemeral workload signals	Add runtime telemetry requirements (Kubernetes audit, control plane logs) to the standard 1
No exception process for missing agents/log sources	Gaps persist and you can’t prove remediation	Create a monitoring exception log with owners and closure evidence 2
Alert fatigue with no tuning governance	Detections become noise; “monitored” becomes “ignored”	Review noisy rules, document tuning decisions, and retire low-value alerts 2
Evidence scattered across tools	You cannot answer audits quickly	Build a recurring DE.CM-09 evidence bundle; Daydream can track owners, reviews, and artifacts 2

Enforcement context and risk implications

No public enforcement case sources were provided for this requirement in the supplied catalog. From a risk perspective, DE.CM-09 failures tend to show up as:

• Longer time-to-detect and time-to-contain because key events were never collected or never reviewed.
• Inability to support incident investigation because logs are missing, overwritten, or not centralized.
• Failed customer diligence (SOC 2-style questioning) because you cannot demonstrate monitoring coverage and operational follow-through.

Treat DE.CM-09 as a “program credibility” control: it’s easy to claim and hard to evidence without discipline. ²

Practical 30/60/90-day execution plan

First 30 days (stabilize scope + visibility)

• Name an executive owner and operational owners for endpoints, cloud, and applications. ²
• Publish the Monitoring Coverage Matrix v1 for critical assets and top runtimes. ¹
• Enable/confirm core telemetry: endpoint security alerts, cloud control plane audit logs, identity provider logs, and centralized ingestion. ¹
• Draft the Adverse Event Catalog and define triage responsibilities. ²

Days 31–60 (operationalize detections + workflow)

• Build the detection register for the adverse event categories you can support now. ²
• Connect alerts to a case/ticket workflow with required fields (asset, user, disposition, timestamps). ²
• Stand up the exception process for monitoring gaps (missing agents, log ingestion failures, unmanaged accounts). ²
• Run the first control performance review and capture remediation actions. ²

Days 61–90 (prove effectiveness + harden evidence)

• Expand coverage to additional runtimes (Kubernetes/serverless/managed services) and validate telemetry quality. ¹
• Add detection tuning governance: who can change rules, how changes are tested, and how you document outcomes. ²
• Produce an “audit-ready” evidence bundle with sample cases, review minutes, and closure proof for exceptions. ²
• Track metrics in a single place (for example, in Daydream) so you can show trend and management oversight without rebuilding evidence each cycle. ²

Frequently Asked Questions

Does DE.CM-09 require a SIEM?

NIST CSF does not mandate a specific tool, but you must monitor and detect adverse events across hardware, software, runtimes, and data. A SIEM (or equivalent centralized log platform) is the most common way to prove coverage and retain evidence. ¹

What counts as a “runtime environment” for audit purposes?

Treat it broadly: VMs, containers/Kubernetes, serverless, and managed application platforms where code runs and where control plane events matter. Document which runtimes you operate and the telemetry you collect from each. ¹

How do we operationalize “their data are monitored” without monitoring every database query?

Focus on high-signal data events: authentication/authorization logs, admin actions, export jobs, bulk access patterns, and encryption key events. Then map each to an adverse event category and a response path. ²

We outsource IT/SOC functions to a third party. Can we “inherit” DE.CM-09?

You can contract for monitoring and response, but you still need governance evidence: defined scope, reporting, exception handling, and proof of review. Keep the monitoring coverage matrix and sample cases even if the SOC is external. ²

What evidence is fastest to produce for an audit request?

Provide the monitoring coverage matrix, a detection register, and a small set of alert-to-case examples that show triage and disposition. Add the most recent control performance review record with remediation items. ²

How do we show control operation if alerts are rare?

Auditors accept controls that are quiet if you can prove the pipeline works: telemetry health checks, test alerts, documented review cadence, and closed exceptions for monitoring gaps. Keep records of test events and the resulting tickets. ²

Footnotes

1. NIST CSWP 29

2. NIST CSF 2.0

3. NIST CSF 1.1 to 2.0 Core Transition Changes